Rank the globular clusters in the table by the ease of separating the cluster members from the field stars, using the Gaia DR3.
Here is the first example: NGC 6544. The database of each globular cluster can be explored here.
Variables used: source_id, ra, dec, parallax, pmra, pmdec , bp_rp, mh_gspphot, radial_velocity, phot_g_mean_mag.
ngc_6544 <- read.csv("1657065339571O-result.csv")
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.7 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(readr)
library(ggplot2)
library(ggpubr)
summary(ngc_6544)
## source_id ra dec parallax
## Min. :4.066e+18 Min. :271.8 Min. :-25.03 Min. :-5.8994
## 1st Qu.:4.066e+18 1st Qu.:271.8 1st Qu.:-25.02 1st Qu.: 0.0246
## Median :4.066e+18 Median :271.8 Median :-25.01 Median : 0.3166
## Mean :4.066e+18 Mean :271.8 Mean :-25.01 Mean : 0.2974
## 3rd Qu.:4.066e+18 3rd Qu.:271.8 3rd Qu.:-25.01 3rd Qu.: 0.5426
## Max. :4.066e+18 Max. :271.9 Max. :-24.98 Max. : 5.6389
## NA's :884
## pmra pmdec ruwe phot_g_mean_mag
## Min. :-11.516 Min. :-26.763 Min. : 0.6191 Min. :11.30
## 1st Qu.: -3.111 1st Qu.:-18.901 1st Qu.: 1.0156 1st Qu.:18.01
## Median : -2.288 Median :-18.220 Median : 1.1075 Median :18.66
## Mean : -2.270 Mean :-14.553 Mean : 1.3995 Mean :18.49
## 3rd Qu.: -1.480 3rd Qu.: -8.008 3rd Qu.: 1.3960 3rd Qu.:19.29
## Max. : 13.098 Max. : 2.831 Max. :10.1384 Max. :20.46
## NA's :884 NA's :884 NA's :884 NA's :6
## bp_rp radial_velocity phot_variable_flag non_single_star
## Min. :0.3908 Min. :-39.574 Length:2000 Min. :0
## 1st Qu.:1.7304 1st Qu.:-37.722 Class :character 1st Qu.:0
## Median :1.8792 Median :-31.857 Mode :character Median :0
## Mean :2.0130 Mean :-27.274 Mean :0
## 3rd Qu.:2.1718 3rd Qu.:-21.378 3rd Qu.:0
## Max. :4.5840 Max. : -2.793 Max. :0
## NA's :1191 NA's :1994
## has_xp_continuous has_xp_sampled has_rvs has_epoch_photometry
## Length:2000 Length:2000 Length:2000 Length:2000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## has_epoch_rv has_mcmc_gspphot has_mcmc_msc teff_gspphot
## Length:2000 Length:2000 Length:2000 Min. : 3368
## Class :character Class :character Class :character 1st Qu.: 4239
## Mode :character Mode :character Mode :character Median : 4808
## Mean : 5409
## 3rd Qu.: 5721
## Max. :15003
## NA's :1809
## logg_gspphot mh_gspphot distance_gspphot azero_gspphot
## Min. :0.4915 Min. :-4.0890 Min. : 338.4 Min. :0.0056
## 1st Qu.:3.6568 1st Qu.:-3.3621 1st Qu.: 766.6 1st Qu.:1.1229
## Median :4.2829 Median :-1.4194 Median : 958.6 Median :2.0239
## Mean :4.0768 Mean :-1.8236 Mean :1408.2 Mean :2.5044
## 3rd Qu.:4.6778 3rd Qu.:-0.7768 3rd Qu.:1497.2 3rd Qu.:3.6508
## Max. :5.0177 Max. : 0.7731 Max. :9382.5 Max. :8.8559
## NA's :1809 NA's :1809 NA's :1809 NA's :1809
## ag_gspphot ebpminrp_gspphot
## Min. :0.0041 Min. :0.0022
## 1st Qu.:0.8386 1st Qu.:0.4668
## Median :1.5482 Median :0.8514
## Mean :1.8315 Mean :1.0174
## 3rd Qu.:2.7957 3rd Qu.:1.5431
## Max. :5.8228 Max. :3.3140
## NA's :1809 NA's :1809
lm() to regress ra on dec and save the regression as model_1.model_1 <- lm(ra ~ dec, data = ngc_6544)
summary().An increase of one unit of dec is associated with an additional -0.22698 unit decrease in ra. This relationship is statistically significant at < 0.001.
summary(model_1)
##
## Call:
## lm(formula = ra ~ dec, data = ngc_6544)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.032100 -0.014882 0.002286 0.013487 0.039536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 266.15652 1.09471 243.130 < 2e-16 ***
## dec -0.22698 0.04376 -5.186 2.36e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01676 on 1998 degrees of freedom
## Multiple R-squared: 0.01328, Adjusted R-squared: 0.01279
## F-statistic: 26.9 on 1 and 1998 DF, p-value: 2.362e-07
model_1.ggplot(data = model_1, aes(x = dec, y = ra)) +
geom_point(alpha=0.5, size=2, color = 'orange') +
labs(y="ra", x="dec") +
stat_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
ngc_6544 %>%
ggplot(aes(dec,ra)) +
geom_point(alpha=0.5, size=2, color = 'blue') +
labs(y="ra", x="dec")
lm() to regress pmra on pmdec and save the regression as model_2.model_2 <- lm(pmra ~ pmdec, data = ngc_6544)
summary().An increase of one unit of pmdec is associated with an additional 0.058145 unit increase in pmra. This relationship is statistically significant at < 0.001.
summary(model_2)
##
## Call:
## lm(formula = pmra ~ pmdec, data = ngc_6544)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.1656 -0.6862 0.1299 0.8668 15.2929
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.423818 0.150293 -9.474 < 2e-16 ***
## pmdec 0.058145 0.009366 6.208 7.54e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.115 on 1114 degrees of freedom
## (884 observations deleted due to missingness)
## Multiple R-squared: 0.03344, Adjusted R-squared: 0.03257
## F-statistic: 38.54 on 1 and 1114 DF, p-value: 7.545e-10
model_2.ggplot(data = model_2, aes(x = pmdec, y = pmra)) +
geom_point(alpha=0.5, size=2, color = 'orange') +
labs(y="pmra", x="pmdec") +
stat_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
ngc_6544 %>%
ggplot(aes(pmdec,pmra)) +
geom_point(alpha=0.5, size=2, color = 'blue') +
labs(y="pmra", x="pmdec")
## Warning: Removed 884 rows containing missing values (geom_point).
lm() to regress phot_g_mean_mag on bp_rp and save the regression as model_3.model_3 <- lm(phot_g_mean_mag ~ bp_rp, data = ngc_6544)
summary().An increase of one unit of bp_rp is associated with an additional -0.02021 unit decrease in phot_g_mean_mag. This relationship is statistically significant at < 1.
summary(model_3)
##
## Call:
## lm(formula = phot_g_mean_mag ~ bp_rp, data = ngc_6544)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.4813 -0.4299 0.2757 0.7987 2.3602
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.78717 0.17697 100.512 <2e-16 ***
## bp_rp -0.02021 0.08514 -0.237 0.812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.255 on 807 degrees of freedom
## (1191 observations deleted due to missingness)
## Multiple R-squared: 6.981e-05, Adjusted R-squared: -0.001169
## F-statistic: 0.05634 on 1 and 807 DF, p-value: 0.8124
model_3.ggplot(data = model_3, aes(x = bp_rp, y = phot_g_mean_mag)) +
geom_point(alpha=0.5, size=2, color = 'orange') +
labs(y="phot_g_mean_mag", x="bp_rp") +
stat_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ngc_6544 %>%
ggplot(aes(bp_rp,phot_g_mean_mag)) +
geom_point(alpha=0.5, size=2, color = 'blue') +
labs(y="phot_g_mean_mag", x="bp_rp")
## Warning: Removed 1191 rows containing missing values (geom_point).
ggplot(ngc_6544, aes(mh_gspphot)) +
geom_histogram(bins = 30)
## Warning: Removed 1809 rows containing non-finite values (stat_bin).
ggplot(ngc_6544, aes(radial_velocity)) +
geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1994 rows containing non-finite values (stat_bin).
ngc_6553 <- read_csv("1657165215641O-result.csv")
## Rows: 2000 Columns: 26
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): phot_variable_flag
## dbl (18): source_id, ra, dec, parallax, pmra, pmdec, ruwe, phot_g_mean_mag, ...
## lgl (7): has_xp_continuous, has_xp_sampled, has_rvs, has_epoch_photometry, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
summary(ngc_6553)
## source_id ra dec parallax
## Min. :4.065e+18 Min. :272.3 Min. :-25.94 Min. :-8.3416
## 1st Qu.:4.065e+18 1st Qu.:272.3 1st Qu.:-25.93 1st Qu.:-0.0843
## Median :4.065e+18 Median :272.3 Median :-25.92 Median : 0.1786
## Mean :4.065e+18 Mean :272.3 Mean :-25.92 Mean : 0.2657
## 3rd Qu.:4.065e+18 3rd Qu.:272.3 3rd Qu.:-25.91 3rd Qu.: 0.6172
## Max. :4.065e+18 Max. :272.4 Max. :-25.89 Max. : 8.1596
## NA's :1027
## pmra pmdec ruwe phot_g_mean_mag
## Min. :-10.6486 Min. :-13.6290 Min. : 0.5672 Min. :12.16
## 1st Qu.: -1.6654 1st Qu.: -3.3308 1st Qu.: 1.1090 1st Qu.:16.83
## Median : 0.1145 Median : -0.7711 Median : 1.3851 Median :18.25
## Mean : -0.5553 Mean : -1.7958 Mean : 1.7882 Mean :17.82
## 3rd Qu.: 0.7289 3rd Qu.: -0.1012 3rd Qu.: 1.9862 3rd Qu.:18.99
## Max. : 8.9418 Max. : 10.3322 Max. :17.6072 Max. :19.98
## NA's :1027 NA's :1027 NA's :1027 NA's :3
## bp_rp radial_velocity phot_variable_flag non_single_star
## Min. :-0.1685 Min. :-141.1828 Length:2000 Min. :0
## 1st Qu.: 1.9018 1st Qu.: -1.8062 Class :character 1st Qu.:0
## Median : 2.0406 Median : 0.6087 Mode :character Median :0
## Mean : 2.0841 Mean : -11.7559 Mean :0
## 3rd Qu.: 2.2114 3rd Qu.: 8.9440 3rd Qu.:0
## Max. : 4.6823 Max. : 21.6074 Max. :0
## NA's :1181 NA's :1991
## has_xp_continuous has_xp_sampled has_rvs has_epoch_photometry
## Mode :logical Mode :logical Mode :logical Mode :logical
## FALSE:1744 FALSE:1945 FALSE:1997 FALSE:1967
## TRUE :256 TRUE :55 TRUE :3 TRUE :33
##
##
##
##
## has_epoch_rv has_mcmc_gspphot has_mcmc_msc teff_gspphot
## Mode :logical Mode :logical Mode :logical Min. : 3543
## FALSE:2000 FALSE:1910 FALSE:1363 1st Qu.: 4762
## TRUE :90 TRUE :637 Median : 4986
## Mean : 5299
## 3rd Qu.: 5256
## Max. :29741
## NA's :1870
## logg_gspphot mh_gspphot distance_gspphot azero_gspphot
## Min. :0.0369 Min. :-4.0553 Min. : 416 Min. :0.4017
## 1st Qu.:2.8365 1st Qu.:-0.9629 1st Qu.: 974 1st Qu.:2.4291
## Median :3.3775 Median :-0.4138 Median : 1611 Median :3.0437
## Mean :3.2869 Mean :-0.6261 Mean : 2064 Mean :3.1867
## 3rd Qu.:4.0656 3rd Qu.:-0.0277 3rd Qu.: 2141 3rd Qu.:3.8114
## Max. :4.9183 Max. : 0.7808 Max. :10902 Max. :9.7627
## NA's :1870 NA's :1870 NA's :1870 NA's :1870
## ag_gspphot ebpminrp_gspphot
## Min. :0.3346 Min. :0.1828
## 1st Qu.:1.8167 1st Qu.:0.9804
## Median :2.1996 Median :1.1942
## Mean :2.2988 Mean :1.2650
## 3rd Qu.:2.7175 3rd Qu.:1.4837
## Max. :6.1531 Max. :3.5492
## NA's :1870 NA's :1870
lm() to regress ra on dec and save the regression as model_4.model_4 <- lm(ra ~ dec, data = ngc_6553)
summary().An increase of one unit of dec is associated with an additional 0.64366 unit increase in ra. This relationship is statistically significant at < 0.001.
summary(model_4)
##
## Call:
## lm(formula = ra ~ dec, data = ngc_6553)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0293835 -0.0080779 -0.0003821 0.0079258 0.0215398
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 289.01202 0.52899 546.34 <2e-16 ***
## dec 0.64366 0.02041 31.54 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01013 on 1998 degrees of freedom
## Multiple R-squared: 0.3324, Adjusted R-squared: 0.332
## F-statistic: 994.6 on 1 and 1998 DF, p-value: < 2.2e-16
model_4.ggplot(data = model_4, aes(x = dec, y = ra)) +
geom_point(alpha=0.5, size=2, color = 'orange') +
labs(y="ra", x="dec") +
stat_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
ngc_6553 %>%
ggplot(aes(dec,ra)) +
geom_point(alpha=0.5, size= 2, color = 'blue') +
labs(y="ra", x="dec")
#### Model 5
lm() to regress pmra on pmdec and save the regression as model_5.model_5 <- lm(pmra ~ pmdec, data = ngc_6553)
summary().An increase of one unit of pmdec is associated with an additional 0.46002 unit increase in pmra. This relationship is statistically significant at < 0.001.
summary(model_5)
##
## Call:
## lm(formula = pmra ~ pmdec, data = ngc_6553)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.1661 -0.6839 0.1666 0.8702 9.5786
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.27083 0.08168 3.316 0.000948 ***
## pmdec 0.46002 0.02357 19.514 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.179 on 971 degrees of freedom
## (1027 observations deleted due to missingness)
## Multiple R-squared: 0.2817, Adjusted R-squared: 0.2809
## F-statistic: 380.8 on 1 and 971 DF, p-value: < 2.2e-16
c.Plot results from model_5.
ggplot(data = model_5, aes(x = pmdec, y = pmra)) +
geom_point(alpha=0.5, size=2, color = 'orange') +
labs(y="pmra", x="pmdec") +
stat_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ngc_6553 %>%
ggplot(aes(pmdec,pmra)) +
geom_point(alpha=0.5, size=2, color = 'blue') +
labs(y="pmra", x="pmdec")
## Warning: Removed 1027 rows containing missing values (geom_point).
lm() to regress pmra on pmdec and save the regression as model_6.model_6 <- lm(phot_g_mean_mag ~ bp_rp, data = ngc_6553)
summary().An increase of one unit of pmdec is associated with an additional -1.8339 unit decrease in pmra. This relationship is statistically significant at < 0.001.
summary(model_6)
##
## Call:
## lm(formula = phot_g_mean_mag ~ bp_rp, data = ngc_6553)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0832 -1.0476 0.0901 0.9477 3.8523
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.7853 0.2251 92.35 <2e-16 ***
## bp_rp -1.8339 0.1059 -17.32 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.263 on 817 degrees of freedom
## (1181 observations deleted due to missingness)
## Multiple R-squared: 0.2685, Adjusted R-squared: 0.2676
## F-statistic: 299.9 on 1 and 817 DF, p-value: < 2.2e-16
model_6.ggplot(data = model_6, aes(x = bp_rp, y = phot_g_mean_mag)) +
geom_point(alpha=0.5, size=2, color = 'orange') +
labs(y="phot_g_mean_mag", x="bp_rp") +
stat_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ngc_6553 %>%
ggplot(aes(bp_rp,phot_g_mean_mag)) +
geom_point(alpha=0.5, size=2, color = 'blue') +
labs(y="phot_g_mean_mag", x="bp_rp")
## Warning: Removed 1181 rows containing missing values (geom_point).
ggplot(ngc_6553, aes(mh_gspphot)) +
geom_histogram(bins = 30)
## Warning: Removed 1870 rows containing non-finite values (stat_bin).
ggplot(ngc_6553, aes(radial_velocity)) +
geom_histogram(bins = 30)
## Warning: Removed 1991 rows containing non-finite values (stat_bin).
terzan_12 <- read_csv("1657165792389O-result.csv")
## Rows: 1416 Columns: 26
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): phot_variable_flag
## dbl (18): source_id, ra, dec, parallax, pmra, pmdec, ruwe, phot_g_mean_mag, ...
## lgl (7): has_xp_continuous, has_xp_sampled, has_rvs, has_epoch_photometry, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
summary(terzan_12)
## source_id ra dec parallax
## Min. :4.067e+18 Min. :273.0 Min. :-22.78 Min. :-7.4591
## 1st Qu.:4.067e+18 1st Qu.:273.1 1st Qu.:-22.75 1st Qu.:-0.0605
## Median :4.067e+18 Median :273.1 Median :-22.74 Median : 0.2401
## Mean :4.067e+18 Mean :273.1 Mean :-22.74 Mean : 0.3113
## 3rd Qu.:4.067e+18 3rd Qu.:273.1 3rd Qu.:-22.73 3rd Qu.: 0.6288
## Max. :4.067e+18 Max. :273.1 Max. :-22.71 Max. : 9.1725
## NA's :330
## pmra pmdec ruwe phot_g_mean_mag
## Min. :-14.0406 Min. :-16.476 Min. :0.7628 Min. :13.49
## 1st Qu.: -6.1099 1st Qu.: -5.272 1st Qu.:1.0056 1st Qu.:18.77
## Median : -3.7502 Median : -3.277 Median :1.0593 Median :19.67
## Mean : -3.2932 Mean : -3.822 Mean :1.0987 Mean :19.43
## 3rd Qu.: -0.7696 3rd Qu.: -2.516 3rd Qu.:1.1222 3rd Qu.:20.40
## Max. : 13.0895 Max. : 8.646 Max. :7.9593 Max. :21.31
## NA's :330 NA's :330 NA's :330 NA's :18
## bp_rp radial_velocity phot_variable_flag non_single_star
## Min. :-1.595 Min. :-56.708 Length:1416 Min. :0
## 1st Qu.: 2.646 1st Qu.:-28.504 Class :character 1st Qu.:0
## Median : 3.212 Median : 4.224 Mode :character Median :0
## Mean : 3.165 Mean : 26.071 Mean :0
## 3rd Qu.: 3.713 3rd Qu.: 90.723 3rd Qu.:0
## Max. : 6.977 Max. :121.787 Max. :0
## NA's :380 NA's :1403
## has_xp_continuous has_xp_sampled has_rvs has_epoch_photometry
## Mode :logical Mode :logical Mode :logical Mode :logical
## FALSE:1306 FALSE:1405 FALSE:1416 FALSE:1394
## TRUE :110 TRUE :11 TRUE :22
##
##
##
##
## has_epoch_rv has_mcmc_gspphot has_mcmc_msc teff_gspphot
## Mode :logical Mode :logical Mode :logical Min. : 3133
## FALSE:1416 FALSE:1198 FALSE:1230 1st Qu.: 3976
## TRUE :218 TRUE :186 Median : 4528
## Mean : 4689
## 3rd Qu.: 4799
## Max. :15012
## NA's :1157
## logg_gspphot mh_gspphot distance_gspphot azero_gspphot
## Min. :0.2009 Min. :-3.7779 Min. : 306.4 Min. : 0.0498
## 1st Qu.:3.2127 1st Qu.:-0.8198 1st Qu.: 512.4 1st Qu.: 5.1394
## Median :4.0442 Median :-0.3749 Median : 868.7 Median : 6.7906
## Mean :3.5905 Mean :-0.3844 Mean :1107.3 Mean : 6.2606
## 3rd Qu.:4.1676 3rd Qu.: 0.2052 3rd Qu.:1175.7 3rd Qu.: 8.1680
## Max. :4.7671 Max. : 0.7830 Max. :8928.4 Max. : 9.9999
## NA's :1157 NA's :1157 NA's :1157 NA's :1157
## ag_gspphot ebpminrp_gspphot
## Min. :0.0377 Min. :0.0201
## 1st Qu.:3.3614 1st Qu.:1.8909
## Median :4.3724 Median :2.4866
## Mean :4.0440 Mean :2.2990
## 3rd Qu.:5.2477 3rd Qu.:2.9979
## Max. :6.6393 Max. :3.8421
## NA's :1157 NA's :1157
lm() to regress ra on dec and save the regression as model_7.model_7 <- lm(ra ~ dec, data = terzan_12)
summary().An increase of one unit of dec is associated with an additional 0.01603 unit increase in ra. This relationship is statistically significant at < 1.
summary(model_7)
##
## Call:
## lm(formula = ra ~ dec, data = terzan_12)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03239 -0.01213 -0.00112 0.01108 0.03927
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 273.42692 0.64806 421.918 <2e-16 ***
## dec 0.01603 0.02850 0.562 0.574
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01648 on 1414 degrees of freedom
## Multiple R-squared: 0.0002237, Adjusted R-squared: -0.0004834
## F-statistic: 0.3164 on 1 and 1414 DF, p-value: 0.5739
model_7.ggplot(data = model_7, aes(x = dec, y = ra)) +
geom_point(alpha=0.5, size=2, color = 'orange') +
labs(y="ra", x="dec") +
stat_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
terzan_12 %>%
ggplot(aes(dec,ra)) +
geom_point(alpha=0.5, size= 2, color = 'blue') +
labs(y="ra", x="dec")
lm() to regress pmra on pmdec and save the regression as model_8.model_8 <- lm(pmra ~ pmdec, data = terzan_12)
summary().An increase of one unit of pmdec is associated with an additional 0.17153 unit increase in pmra. This relationship is statistically significant at < 0.001.
summary(model_8)
##
## Call:
## lm(formula = pmra ~ pmdec, data = terzan_12)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.9358 -2.8916 -0.3413 2.4903 17.9565
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.63767 0.16686 -15.807 < 2e-16 ***
## pmdec 0.17153 0.03461 4.956 8.33e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.352 on 1084 degrees of freedom
## (330 observations deleted due to missingness)
## Multiple R-squared: 0.02216, Adjusted R-squared: 0.02126
## F-statistic: 24.56 on 1 and 1084 DF, p-value: 8.334e-07
model_8.ggplot(data = model_8, aes(x = pmdec, y = pmra)) +
geom_point(alpha=0.5, size=2, color = 'orange') +
labs(y="pmra", x="pmdec") +
stat_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
terzan_12 %>%
ggplot(aes(pmdec,pmra)) +
geom_point(alpha=0.5, size=2, color = 'blue') +
labs(y="pmra", x="pmdec")
## Warning: Removed 330 rows containing missing values (geom_point).
ggplot(terzan_12, aes(mh_gspphot)) +
geom_histogram(bins = 30)
## Warning: Removed 1157 rows containing non-finite values (stat_bin).
ggplot(terzan_12, aes(radial_velocity)) +
geom_histogram(bins = 30)
## Warning: Removed 1403 rows containing non-finite values (stat_bin).
ngc_6380 <- read_csv("1657166932125O-result.csv")
## Rows: 2000 Columns: 26
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): phot_variable_flag
## dbl (18): source_id, ra, dec, parallax, pmra, pmdec, ruwe, phot_g_mean_mag, ...
## lgl (7): has_xp_continuous, has_xp_sampled, has_rvs, has_epoch_photometry, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
summary(ngc_6380)
## source_id ra dec parallax
## Min. :5.962e+18 Min. :263.6 Min. :-39.10 Min. :-10.4104
## 1st Qu.:5.962e+18 1st Qu.:263.6 1st Qu.:-39.08 1st Qu.: -0.2638
## Median :5.962e+18 Median :263.6 Median :-39.07 Median : 0.0929
## Mean :5.962e+18 Mean :263.6 Mean :-39.08 Mean : 0.0708
## 3rd Qu.:5.962e+18 3rd Qu.:263.6 3rd Qu.:-39.07 3rd Qu.: 0.4979
## Max. :5.962e+18 Max. :263.7 Max. :-39.05 Max. : 9.7858
## NA's :785
## pmra pmdec ruwe phot_g_mean_mag
## Min. :-16.750 Min. :-17.015 Min. :0.7097 Min. : 9.705
## 1st Qu.: -2.776 1st Qu.: -4.196 1st Qu.:1.0160 1st Qu.:18.206
## Median : -2.127 Median : -3.345 Median :1.0820 Median :19.232
## Mean : -1.929 Mean : -3.492 Mean :1.2662 Mean :19.055
## 3rd Qu.: -1.087 3rd Qu.: -2.739 3rd Qu.:1.2366 3rd Qu.:20.150
## Max. : 14.119 Max. : 6.418 Max. :9.3613 Max. :21.159
## NA's :785 NA's :785 NA's :785 NA's :5
## bp_rp radial_velocity phot_variable_flag non_single_star
## Min. :-1.396 Min. :-208.995 Length:2000 Min. :0
## 1st Qu.: 2.259 1st Qu.: -4.916 Class :character 1st Qu.:0
## Median : 2.590 Median : 5.294 Mode :character Median :0
## Mean : 2.510 Mean : -12.601 Mean :0
## 3rd Qu.: 2.769 3rd Qu.: 13.563 3rd Qu.:0
## Max. : 5.867 Max. : 51.103 Max. :0
## NA's :972 NA's :1982
## has_xp_continuous has_xp_sampled has_rvs has_epoch_photometry
## Mode :logical Mode :logical Mode :logical Mode :logical
## FALSE:1834 FALSE:1982 FALSE:1999 FALSE:1959
## TRUE :166 TRUE :18 TRUE :1 TRUE :41
##
##
##
##
## has_epoch_rv has_mcmc_gspphot has_mcmc_msc teff_gspphot
## Mode :logical Mode :logical Mode :logical Min. : 3417
## FALSE:2000 FALSE:1790 FALSE:1585 1st Qu.: 3893
## TRUE :210 TRUE :415 Median : 4300
## Mean : 4738
## 3rd Qu.: 4789
## Max. :15017
## NA's :1755
## logg_gspphot mh_gspphot distance_gspphot azero_gspphot
## Min. :0.4522 Min. :-3.1950 Min. : 330.3 Min. :0.0159
## 1st Qu.:4.0714 1st Qu.:-1.3783 1st Qu.: 566.5 1st Qu.:2.2810
## Median :4.2419 Median :-1.0010 Median : 677.6 Median :3.1939
## Mean :4.0669 Mean :-0.9318 Mean : 977.2 Mean :3.3982
## 3rd Qu.:4.4333 3rd Qu.:-0.5022 3rd Qu.:1048.5 3rd Qu.:4.3835
## Max. :4.9295 Max. : 0.7855 Max. :7690.7 Max. :9.9591
## NA's :1755 NA's :1755 NA's :1755 NA's :1755
## ag_gspphot ebpminrp_gspphot
## Min. :0.0111 Min. :0.0060
## 1st Qu.:1.6090 1st Qu.:0.8926
## Median :2.2064 Median :1.2149
## Mean :2.3579 Mean :1.3079
## 3rd Qu.:3.0044 3rd Qu.:1.6493
## Max. :6.0713 Max. :3.4802
## NA's :1755 NA's :1755
lm() to regress ra on dec and save the regression as model_9.model_9 <- lm(ra ~ dec, data = ngc_6380)
summary().An increase of one unit of dec is associated with an additional 0.35053 unit increase in ra. This relationship is statistically significant at < 0.001.
summary(model_9)
##
## Call:
## lm(formula = ra ~ dec, data = ngc_6380)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043668 -0.010389 -0.001172 0.011399 0.037492
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 277.31808 1.34662 205.94 <2e-16 ***
## dec 0.35053 0.03446 10.17 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01671 on 1998 degrees of freedom
## Multiple R-squared: 0.04923, Adjusted R-squared: 0.04876
## F-statistic: 103.5 on 1 and 1998 DF, p-value: < 2.2e-16
model_9.ggplot(data = model_9, aes(x = dec, y = ra)) +
geom_point(alpha=0.5, size=2, color = 'orange') +
labs(y="ra", x="dec") +
stat_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
ngc_6380 %>%
ggplot(aes(dec,ra)) +
geom_point(alpha=0.5, size= 2, color = 'blue') +
labs(y="ra", x="dec")
lm() to regress pmra on pmdec and save the regression as model_10.model_10 <- lm(pmra ~ pmdec, data = ngc_6380)
summary().An increase of one unit of pmdec is associated with an additional 0.17272 unit increase in pmra. This relationship is statistically significant at < 0.001.
summary(model_10)
##
## Call:
## lm(formula = pmra ~ pmdec, data = ngc_6380)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.0337 -0.9047 -0.2010 0.8336 15.0940
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.32546 0.12673 -10.459 < 2e-16 ***
## pmdec 0.17272 0.03098 5.576 3.03e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.302 on 1213 degrees of freedom
## (785 observations deleted due to missingness)
## Multiple R-squared: 0.02499, Adjusted R-squared: 0.02419
## F-statistic: 31.09 on 1 and 1213 DF, p-value: 3.034e-08
model_10.ggplot(data = model_10, aes(x = pmdec, y = pmra)) +
geom_point(alpha=0.5, size=2, color = 'orange') +
labs(y="pmra", x="pmdec") +
stat_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
ngc_6380 %>%
ggplot(aes(pmdec,pmra)) +
geom_point(alpha=0.5, size=2, color = 'blue') +
labs(y="pmra", x="pmdec")
## Warning: Removed 785 rows containing missing values (geom_point).
lm() to regress phot_g_mean_mag on bp_rp and save the regression as model_11.model_11 <- lm(phot_g_mean_mag ~ bp_rp, data = ngc_6380)
summary().An increase of one unit of bp_rp is associated with an additional -1.02741 unit decrease in phot_g_mean_mag. This relationship is statistically significant at < 0.001.
summary(model_11)
##
## Call:
## lm(formula = phot_g_mean_mag ~ bp_rp, data = ngc_6380)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.0059 -0.6643 -0.0116 0.9888 3.5449
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21.18548 0.22031 96.16 <2e-16 ***
## bp_rp -1.02741 0.08615 -11.93 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.356 on 1026 degrees of freedom
## (972 observations deleted due to missingness)
## Multiple R-squared: 0.1217, Adjusted R-squared: 0.1209
## F-statistic: 142.2 on 1 and 1026 DF, p-value: < 2.2e-16
model_11.ggplot(data = model_11, aes(x = bp_rp, y = phot_g_mean_mag)) +
geom_point(alpha=0.5, size=2, color = 'orange') +
labs(y="phot_g_mean_mag", x="bp_rp") +
stat_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
ngc_6380 %>%
ggplot(aes(bp_rp,phot_g_mean_mag)) +
geom_point(alpha=0.5, size=2, color = 'blue') +
labs(y="phot_g_mean_mag", x="bp_rp")
## Warning: Removed 972 rows containing missing values (geom_point).
ggplot(ngc_6380, aes(mh_gspphot)) +
geom_histogram(bins = 30)
## Warning: Removed 1755 rows containing non-finite values (stat_bin).
ggplot(ngc_6380, aes(radial_velocity)) +
geom_histogram(bins = 30)
## Warning: Removed 1982 rows containing non-finite values (stat_bin).
fsr_1758 <- read.csv("1657248340808O-result.csv")
summary(fsr_1758)
## source_id ra dec parallax
## Min. :5.962e+18 Min. :262.8 Min. :-39.86 Min. :-12.0892
## 1st Qu.:5.962e+18 1st Qu.:262.8 1st Qu.:-39.84 1st Qu.: -0.2286
## Median :5.962e+18 Median :262.8 Median :-39.83 Median : 0.1495
## Mean :5.962e+18 Mean :262.8 Mean :-39.83 Mean : 0.2072
## 3rd Qu.:5.962e+18 3rd Qu.:262.8 3rd Qu.:-39.82 3rd Qu.: 0.6315
## Max. :5.962e+18 Max. :262.8 Max. :-39.80 Max. : 10.7769
## NA's :990
## pmra pmdec ruwe phot_g_mean_mag
## Min. :-19.8029 Min. :-20.186 Min. : 0.7775 Min. :13.19
## 1st Qu.: -3.6099 1st Qu.: -5.225 1st Qu.: 1.0009 1st Qu.:18.82
## Median : -2.5601 Median : -2.874 Median : 1.0634 Median :19.87
## Mean : -2.2823 Mean : -2.319 Mean : 1.2112 Mean :19.40
## 3rd Qu.: -0.6576 3rd Qu.: 1.918 3rd Qu.: 1.1871 3rd Qu.:20.31
## Max. : 19.9996 Max. : 8.181 Max. :15.3358 Max. :21.06
## NA's :990 NA's :990 NA's :990 NA's :10
## bp_rp radial_velocity phot_variable_flag non_single_star
## Min. :-1.160 Min. :-220.418 Length:2000 Min. :0
## 1st Qu.: 1.628 1st Qu.: -34.066 Class :character 1st Qu.:0
## Median : 1.868 Median : -6.326 Mode :character Median :0
## Mean : 1.836 Mean : 48.350 Mean :0
## 3rd Qu.: 2.059 3rd Qu.: 217.536 3rd Qu.:0
## Max. : 4.722 Max. : 230.887 Max. :0
## NA's :970 NA's :1981
## has_xp_continuous has_xp_sampled has_rvs has_epoch_photometry
## Length:2000 Length:2000 Length:2000 Length:2000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## has_epoch_rv has_mcmc_gspphot has_mcmc_msc teff_gspphot
## Length:2000 Length:2000 Length:2000 Min. : 3275
## Class :character Class :character Class :character 1st Qu.: 3934
## Mode :character Mode :character Mode :character Median : 4431
## Mean : 4616
## 3rd Qu.: 4923
## Max. :15013
## NA's :1717
## logg_gspphot mh_gspphot distance_gspphot azero_gspphot
## Min. :1.628 Min. :-4.1148 Min. : 351.1 Min. :0.0008
## 1st Qu.:4.180 1st Qu.:-1.5883 1st Qu.: 615.8 1st Qu.:0.4348
## Median :4.521 Median :-1.1630 Median : 751.2 Median :1.2773
## Mean :4.338 Mean :-1.1496 Mean :1119.6 Mean :1.4542
## 3rd Qu.:4.811 3rd Qu.:-0.5429 3rd Qu.:1374.4 3rd Qu.:2.2539
## Max. :5.055 Max. : 0.7718 Max. :9314.1 Max. :5.1196
## NA's :1717 NA's :1717 NA's :1717 NA's :1717
## ag_gspphot ebpminrp_gspphot
## Min. :0.0007 Min. :0.0004
## 1st Qu.:0.3161 1st Qu.:0.1708
## Median :0.9321 Median :0.5068
## Mean :1.0716 Mean :0.5831
## 3rd Qu.:1.6457 3rd Qu.:0.8967
## Max. :4.2022 Max. :2.3293
## NA's :1717 NA's :1717
lm() to regress ra on dec and save the regression as model_12.model_12 <- lm(ra ~ dec, data = fsr_1758)
summary().An increase of one unit of dec is associated with an additional 0.59648 unit increase in ra. This relationship is statistically significant at < 0.001.
summary(model_12)
##
## Call:
## lm(formula = ra ~ dec, data = fsr_1758)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.055624 -0.009150 0.004288 0.015665 0.033606
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 286.57239 1.47722 193.99 <2e-16 ***
## dec 0.59648 0.03709 16.08 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02136 on 1998 degrees of freedom
## Multiple R-squared: 0.1146, Adjusted R-squared: 0.1142
## F-statistic: 258.6 on 1 and 1998 DF, p-value: < 2.2e-16
model_12.ggplot(data = model_12, aes(x = dec, y = ra)) +
geom_point(alpha=0.5, size=2, color = 'orange') +
labs(y="ra", x="dec") +
stat_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
fsr_1758 %>%
ggplot(aes(dec,ra)) +
geom_point(alpha=0.5, size=2, color = 'blue') +
labs(y="ra", x="dec")
lm() to regress pmra on pmdec and save the regression as model_13.model_13 <- lm(pmra ~ pmdec, data = fsr_1758)
summary().An increase of one unit of pmdec is associated with an additional 0.04283 unit increase in pmra. This relationship is statistically significant at < 0.1.
summary(model_13)
##
## Call:
## lm(formula = pmra ~ pmdec, data = fsr_1758)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.0701 -1.3090 -0.3629 1.5897 22.7883
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.18301 0.11378 -19.187 <2e-16 ***
## pmdec 0.04283 0.02417 1.772 0.0766 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.147 on 1008 degrees of freedom
## (990 observations deleted due to missingness)
## Multiple R-squared: 0.003107, Adjusted R-squared: 0.002118
## F-statistic: 3.141 on 1 and 1008 DF, p-value: 0.07663
model_13.ggplot(data = model_13, aes(x = pmdec, y = pmra)) +
geom_point(alpha=0.5, size=2, color = 'orange') +
labs(y="pmra", x="pmdec") +
stat_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
fsr_1758 %>%
ggplot(aes(pmdec,pmra)) +
geom_point(alpha=0.5, size=2, color = 'blue') +
labs(y="pmra", x="pmdec")
## Warning: Removed 990 rows containing missing values (geom_point).
lm() to regress phot_g_mean_mag on bp_rp and save the regression as model_14.model_14 <- lm(phot_g_mean_mag ~ bp_rp, data = fsr_1758)
summary().An increase of one unit of bp_rp is associated with an additional -0.44855 unit decrease in phot_g_mean_mag. This relationship is statistically significant at < 0.001.
summary(model_14)
##
## Call:
## lm(formula = phot_g_mean_mag ~ bp_rp, data = fsr_1758)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0683 -0.8107 0.2927 1.0721 2.9958
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.59730 0.17930 109.30 < 2e-16 ***
## bp_rp -0.44855 0.09484 -4.73 2.56e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 1028 degrees of freedom
## (970 observations deleted due to missingness)
## Multiple R-squared: 0.0213, Adjusted R-squared: 0.02034
## F-statistic: 22.37 on 1 and 1028 DF, p-value: 2.565e-06
model_14.ggplot(data = model_14, aes(x = bp_rp, y = phot_g_mean_mag)) +
geom_point(alpha=0.5, size=2, color = 'orange') +
labs(y="phot_g_mean_mag", x="bp_rp") +
stat_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
fsr_1758 %>%
ggplot(aes(bp_rp,phot_g_mean_mag)) +
geom_point(alpha=0.5, size=2, color = 'blue') +
labs(y="phot_g_mean_mag", x="bp_rp")
## Warning: Removed 970 rows containing missing values (geom_point).
ggplot(fsr_1758, aes(mh_gspphot)) +
geom_histogram(bins = 30)
## Warning: Removed 1717 rows containing non-finite values (stat_bin).
ggplot(fsr_1758, aes(radial_velocity)) +
geom_histogram(bins = 30)
## Warning: Removed 1981 rows containing non-finite values (stat_bin).
```